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概率论教程 [（德）凯兰克 著] 2012年版
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概率论教程
作者：（德）凯兰克 著
出版时间：2012年版
内容简介
《概率论教程》是一部讲述现代概率论及其测度论应用基础的教程，其目标读者是该领域的研究生和相关的科研人员。内容广泛，有许多初级教程不能涉及到得的。理论叙述严谨，独立性强。有关测度的部分和概率的章节相互交织，将概率的抽象性完全呈现出来。此外，还有大量的图片、计算模拟、重要数学家的个人传记和大量的例子。这使得表现形式更加活跃。
目录
preface
1 basic measure theory
1.1 classes of sets
1.2 set functior
1.3 the measure exterion theorem
1.4 measurable maps
1.5 random variables
2 independence
2.1 independence of events
2.2 independent random variables
2.3 kolmogorov's 0-1 law
2.4 example：percolation
3 generating functior
3.1 definition and examples
3.2 poisson approximation
3.3 branching processes
4 the integral
4.1 cortruction and simple properties
4.2 monotone convergence and fatou's lemma
.4.3 lebesgue integral verus riemann integral
5 moments and laws of large number
5.1 moments
5.2 weak law of large number
5.3 strong law of large number
5.4 speed of convergence in the strong lln
5.5 the poisson process
6 convergence theorems
6.1 almost sure and measure convergence
6.2 uniform integrability
6.3 exchanging integral and differentiation
7 lp-spaces and the radon-nikodym theorem
7.1 definitior
7.2 inequalities and the fischer-riesz theorem
7.3 hilbert spaces
7.4 lebesgue's decomposition theorem
7.5 supplement：signed measures
7.6 supplement：dual spaces
8 conditional expectatior
8.1 elementary conditional probabilities
8.2 conditional expectatior
8.3 regular conditional distribution
9 martingales
9.1 processes, filtratior, stopping times
9.2 martingales
9.3 discrete stochastic integral
9.4 discrete martingale representation theorem and the crr model
10 optional sampling theorems
10.1 doob decomposition and square variation
10.2 optional sampling and optional stopping
10.3 uniform integrability and optional sampling
11 martingale convergence theorems and their applicatior
11.1 doob's inequality
11.2 martingale convergence theorems
11.3 example：branching process
12 backwards martingales and exchangeability
12.1 exchangeable families of random variables
12.2 backwards martingales
12.3 de finetti's theorem
13 convergence of measures
13.1 a topology primer
13.2 weak and vague convergence
13.3 prohorov's theorem
13.4 application：a fresh look at de finetti's theorem
14 probability measures on product spaces
14.1 product spaces
14.2 finite products and trarition kernels
14.3 kolmogorov's exterion theorem
14.4 markov semigroups
15 characteristic functior and the central limit theorem
15.1 separating classes of functior
15.2 characteristic functior：examples
15.3 l6vy's continuity theorem
15.4 characteristic functior and moments
15.5 the central limit theorem
15.6 multidimerional central limit theorem
16 infinitely divisible distributior
16.1 l6vy-khinchin formula
16.2 stable distributior
17 markov chair
17.1 definitior and cortruction
17.2 discrete markov chair：examples
17.3 discrete markov processes in continuous time
17.4 discrete markov chair：recurrence and trarience
17.5 application：recurrence and trarience of random walks
17.6 invariant distributior
18 convergence of markov chair
18.1 periodicity of markov chair
18.2 coupling and convergence theorem
18.3 markov chain monte carlo method
18.4 speed of convergence
19 markov chair and electrical networks
19.1 harmonic functior
19.2 reverible markov chair
19.3 finite electrical networks
19.4 recurrence and trarience
19.5 network reduction
19.6 random walk in a random environment
20 ergodic theory
20.1 definitior
20.2 ergodic theorems
20.3 examples
20.4 application：recurrence of random walks
20.5 mixing
21 brownian motion
21.1 continuous verior
21.2 cortruction and path properties
21.3 strong markov property
21.4 supplement：feller processes
21.5 cortruction via l2-approximation
21.6 the space c（[0, ∞））
21.7 convergence of probability measures on c（[0, ∞））
21.8 dorker's theorem
21.9 pathwise convergence of branching processes
21.10 square variation and local martingales
22 law of the iterated logarithm
22.l iterated logarithm for the brownian motion
22.2 skorohod's embedding theorem
22.3 hartman-wintner theorem
23 large deviatior
23.1 cramer's theorem
23.2 large deviatior principle
23.3 sanov's theorem
23.4 varadhan's lemma and free energy
24 the poisson point process
24.1 random measures
24.2 properties of the poisson point process
24.3 the poisson-dirichlet distribution
25 the it6 integral
25.1 it6 integral with respect to brownian motion
25.2 it6 integral with respect to diffusior
25.3 the it6 formula
25.4 dirichlet problem and brownian motion
25.5 recurrence and trarience of brownian motion
26 stochastic differential equatior
26.1 strong solutior
26.2 weak solutior and the martingale problem
26.3 weak uniqueness via duality
references
notation index
name index
subject index
作者：（德）凯兰克 著
出版时间：2012年版
内容简介
《概率论教程》是一部讲述现代概率论及其测度论应用基础的教程，其目标读者是该领域的研究生和相关的科研人员。内容广泛，有许多初级教程不能涉及到得的。理论叙述严谨，独立性强。有关测度的部分和概率的章节相互交织，将概率的抽象性完全呈现出来。此外，还有大量的图片、计算模拟、重要数学家的个人传记和大量的例子。这使得表现形式更加活跃。
目录
preface
1 basic measure theory
1.1 classes of sets
1.2 set functior
1.3 the measure exterion theorem
1.4 measurable maps
1.5 random variables
2 independence
2.1 independence of events
2.2 independent random variables
2.3 kolmogorov's 0-1 law
2.4 example：percolation
3 generating functior
3.1 definition and examples
3.2 poisson approximation
3.3 branching processes
4 the integral
4.1 cortruction and simple properties
4.2 monotone convergence and fatou's lemma
.4.3 lebesgue integral verus riemann integral
5 moments and laws of large number
5.1 moments
5.2 weak law of large number
5.3 strong law of large number
5.4 speed of convergence in the strong lln
5.5 the poisson process
6 convergence theorems
6.1 almost sure and measure convergence
6.2 uniform integrability
6.3 exchanging integral and differentiation
7 lp-spaces and the radon-nikodym theorem
7.1 definitior
7.2 inequalities and the fischer-riesz theorem
7.3 hilbert spaces
7.4 lebesgue's decomposition theorem
7.5 supplement：signed measures
7.6 supplement：dual spaces
8 conditional expectatior
8.1 elementary conditional probabilities
8.2 conditional expectatior
8.3 regular conditional distribution
9 martingales
9.1 processes, filtratior, stopping times
9.2 martingales
9.3 discrete stochastic integral
9.4 discrete martingale representation theorem and the crr model
10 optional sampling theorems
10.1 doob decomposition and square variation
10.2 optional sampling and optional stopping
10.3 uniform integrability and optional sampling
11 martingale convergence theorems and their applicatior
11.1 doob's inequality
11.2 martingale convergence theorems
11.3 example：branching process
12 backwards martingales and exchangeability
12.1 exchangeable families of random variables
12.2 backwards martingales
12.3 de finetti's theorem
13 convergence of measures
13.1 a topology primer
13.2 weak and vague convergence
13.3 prohorov's theorem
13.4 application：a fresh look at de finetti's theorem
14 probability measures on product spaces
14.1 product spaces
14.2 finite products and trarition kernels
14.3 kolmogorov's exterion theorem
14.4 markov semigroups
15 characteristic functior and the central limit theorem
15.1 separating classes of functior
15.2 characteristic functior：examples
15.3 l6vy's continuity theorem
15.4 characteristic functior and moments
15.5 the central limit theorem
15.6 multidimerional central limit theorem
16 infinitely divisible distributior
16.1 l6vy-khinchin formula
16.2 stable distributior
17 markov chair
17.1 definitior and cortruction
17.2 discrete markov chair：examples
17.3 discrete markov processes in continuous time
17.4 discrete markov chair：recurrence and trarience
17.5 application：recurrence and trarience of random walks
17.6 invariant distributior
18 convergence of markov chair
18.1 periodicity of markov chair
18.2 coupling and convergence theorem
18.3 markov chain monte carlo method
18.4 speed of convergence
19 markov chair and electrical networks
19.1 harmonic functior
19.2 reverible markov chair
19.3 finite electrical networks
19.4 recurrence and trarience
19.5 network reduction
19.6 random walk in a random environment
20 ergodic theory
20.1 definitior
20.2 ergodic theorems
20.3 examples
20.4 application：recurrence of random walks
20.5 mixing
21 brownian motion
21.1 continuous verior
21.2 cortruction and path properties
21.3 strong markov property
21.4 supplement：feller processes
21.5 cortruction via l2-approximation
21.6 the space c（[0, ∞））
21.7 convergence of probability measures on c（[0, ∞））
21.8 dorker's theorem
21.9 pathwise convergence of branching processes
21.10 square variation and local martingales
22 law of the iterated logarithm
22.l iterated logarithm for the brownian motion
22.2 skorohod's embedding theorem
22.3 hartman-wintner theorem
23 large deviatior
23.1 cramer's theorem
23.2 large deviatior principle
23.3 sanov's theorem
23.4 varadhan's lemma and free energy
24 the poisson point process
24.1 random measures
24.2 properties of the poisson point process
24.3 the poisson-dirichlet distribution
25 the it6 integral
25.1 it6 integral with respect to brownian motion
25.2 it6 integral with respect to diffusior
25.3 the it6 formula
25.4 dirichlet problem and brownian motion
25.5 recurrence and trarience of brownian motion
26 stochastic differential equatior
26.1 strong solutior
26.2 weak solutior and the martingale problem
26.3 weak uniqueness via duality
references
notation index
name index
subject index
